Fast mutual modulation fusion for multi-sensor images

Li, Yufeng; Jiang, Yong; Gao, Lin; Yi, Fan

doi:10.1016/j.ijleo.2014.08.136

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…The dimensionality reduction based medical image fusion methods used for feature processing are PCA [104] and independent component analysis (ICA) [105]. Few authors proposed various approaches of medical image fusion for improvement of fusion performance for better medical diagnosis such as SVM (Support Vector machine) [106], AFSSD [107], FMI based medical image fusion [108], local extrema based medical image fusion [109], NPDM modal [110]. S.L.…”

Section: Other Fusion Methodsmentioning

confidence: 99%

“…This algorithm proves both subjectively and objectively better than DWT, IHS, and HPF methods. Li Yufeng et al [108] proposed modulation-based image fusion. The input images are multiplied with a factor and then combined.…”

Section: Other Fusion Methodsmentioning

confidence: 99%

“…Ø Intensity-hue-saturation (IHS) [22][23][24] Ø Pyramid based fusion methods [25][26] Ø Transform domain-based methods: Other fusion methods Ø ICA, Support vector machine (SVM), etc. [104][105][106][107][108][109][110] spectral information. But pyramid-based methods are not much preferred due to blocking effects, artifacts, and edge uncertainties, that occur by sampling operation.…”

Section: Image Decomposition and Reconstructionmentioning

confidence: 99%

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Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Haribabu

Guruviah

Yogarajah

2023

CMIR

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A Medical imaging plays a vital role in medical diagnosis and clinical treatment. The biggest challenge in the medical field is the correct identification of disease and better treatment. Multi-modal Medical Image Fusion (MMIF) is the process of merging multiple medical images from different modalities into a single fused image. The main objective of the medical image fusion is to obtain a large amount of appropriate information (i.e., features) to improve the quality and makes it more informative for increasing clinical therapy for better diagnosis and clear assessment of medical related problems. The MMIF is generally considered with MRI (Magnetic Resonance Imaging), CT (Computed Tomography), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), MRA (Magnetic Resonance Angiography), T1-weighted MR, T2-weighted MR, X-ray, and ultrasound imaging (Vibro-Acoustography).This review article presents a comprehensive survey of existing medical image fusion methods and have been characterized as six parts: (1) Multi-modality medical images, (2) literature review process, (3) image fusion rules, (4) quality evaluation metrics for assessment of fused image, (5) experimental results on registered datasets and (6) Conclusion. In addition, this review article provides scientific challenges faced in MMIF and future directions for better diagnosis. It is expected that this review will be useful to establish a concrete foundation for developing more valuable fusion methods for medical diagnosis.

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Section: Other Fusion Methodsmentioning

confidence: 99%

Section: Other Fusion Methodsmentioning

confidence: 99%

Section: Image Decomposition and Reconstructionmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Haribabu

Guruviah

Yogarajah

2023

CMIR

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“…In this paper, two source images are magnified by factors that are obtained from the ratio of the corresponding pixel energy. Afterwards, an offset term achieved by calculating statistical parameters of source images is given [15].…”

Section: Related Workmentioning

confidence: 99%

Oil exploration oriented multi-sensor image fusion algorithm

2017

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Abstract:In order to accurately forecast the fracture and fracture dominance direction in oil exploration, in this paper, we propose a novel multi-sensor image fusion algorithm. The main innovations of this paper lie in that we introduce Dual-tree complex wavelet transform (DTCWT) in data fusion and divide an image to several regions before image fusion. DTCWT refers to a new type of wavelet transform, and it is designed to solve the problem of signal decomposition and reconstruction based on two parallel transforms of real wavelet. We utilize DTCWT to segment the features of the input images and generate a region map, and then exploit normalized Shannon entropy of a region to design the priority function. To test the effectiveness of our proposed multi-sensor image fusion algorithm, four standard pairs of images are used to construct the dataset. Experimental results demonstrate that the proposed algorithm can achieve high accuracy in multisensor image fusion, especially for images of oil exploration.

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“…4: Collection class and their sub-classes Network Communication: The subclasses of Network communication are line configuration, biometrics and connectivity[57]. They facilitate secure data transmission using different connectivity for transmitting the information to data centers where it can be accessed by authorized users -represented in Fig.5[58].…”

mentioning

confidence: 99%

A Review-Based Taxonomy for Secure Health Care Monitoring: Wireless Smart Cameras

Batchu

Alsadoon

Prasad

et al. 2021

Journal of Applied Security Research

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Health records data security is one of the main challenges in e-health systems. Authentication is one of the essential security services to support the stored data confidentiality, integrity, and availability. This research focuses on the secure storage of patient and medical records in the healthcare sector where data security and unauthorized access is an ongoing issue. A potential solution comes from biometrics, although their use may be time-consuming and can slow down data retrieval. This research aims to overcome these challenges and enhance data access control in the healthcare sector through the addition of biometrics in the form of fingerprints. The proposed model for application in the healthcare sector consists of Collection, Network communication, and Authentication (CNA) using biometrics, which replaces an existing password-based access control method. A sensor then collects data and by using a network (wireless or Zig-bee), a connection is established, after connectivity analytics and data management work which processes and aggregate the data. Subsequently, access is granted to authenticated users of the application. This IoT-based biometric authentication system facilitates effective recognition and ensures confidentiality, integrity, and reliability of patients' records and other sensitive data. The proposed solution provides reliable access to healthcare data and enables secure access through the process of user and device authentication. The proposed model has been developed for access control to data through the authentication of users in healthcare to reduce data manipulation or theft.

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Fast mutual modulation fusion for multi-sensor images

Cited by 6 publications

References 17 publications

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview

Oil exploration oriented multi-sensor image fusion algorithm

A Review-Based Taxonomy for Secure Health Care Monitoring: Wireless Smart Cameras

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